{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T01:17:12Z","timestamp":1742951832194,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819985456"},{"type":"electronic","value":"9789819985463"}],"license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8546-3_25","type":"book-chapter","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:02:17Z","timestamp":1703530937000},"page":"306-318","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PVT-Crowd: Bridging Multi-scale Features from\u00a0Pyramid Vision Transformer for\u00a0Weakly-Supervised Crowd Counting"],"prefix":"10.1007","author":[{"given":"Zhanqiang","family":"Huo","sequence":"first","affiliation":[]},{"given":"Kunwei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Fen","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Yingxu","family":"Qiao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"25_CR1","unstructured":"Chu, X., et al.: Twins: revisiting the design of spatial attention in vision transformers. In: Advances in Neural Information Processing Systems, vol. 34, pp. 9355\u20139366 (2021)"},{"key":"25_CR2","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., et al.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv e-prints arXiv:2010.11929 (2020)"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Hossain, M., Hosseinzadeh, M., Chanda, O., Wang, Y.: Crowd counting using scale-aware attention networks. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1280\u20131288 (2019)","DOI":"10.1109\/WACV.2019.00141"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Jiang, X., et al.: Attention scaling for crowd counting. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4705\u20134714 (2020)","DOI":"10.1109\/CVPR42600.2020.00476"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Jiang, X., et al.: Crowd counting and density estimation by trellis encoder-decoder networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6126\u20136135 (2019)","DOI":"10.1109\/CVPR.2019.00629"},{"key":"25_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107616","volume":"109","author":"Y Lei","year":"2021","unstructured":"Lei, Y., Liu, Y., Zhang, P., Liu, L.: Towards using count-level weak supervision for crowd counting. Pattern Recogn. 109, 107616 (2021)","journal-title":"Pattern Recogn."},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, X., Chen, D.: CSRNet: dilated convolutional neural networks for understanding the highly congested scenes. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1091\u20131100 (2018)","DOI":"10.1109\/CVPR.2018.00120"},{"issue":"6","key":"25_CR8","doi-asserted-by":"publisher","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)","journal-title":"Sci. China Inf. Sci."},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Lin, H., Ma, Z., Ji, R., Wang, Y., Hong, X.: Boosting crowd counting via multifaceted attention. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19596\u201319605 (2022)","DOI":"10.1109\/CVPR52688.2022.01901"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Ma, Z., Wei, X., Hong, X., Gong, Y.: Bayesian loss for crowd count estimation with point supervision. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 6141\u20136150 (2019)","DOI":"10.1109\/ICCV.2019.00624"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Savner, S.S., Kanhangad, V.: Crowdformer: weakly-supervised crowd counting with improved generalizability (2022). arXiv:2203.03768","DOI":"10.1016\/j.jvcir.2023.103853"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Shi, M., Yang, Z., Xu, C., Chen, Q.: Revisiting perspective information for efficient crowd counting. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7271\u20137280 (2019)","DOI":"10.1109\/CVPR.2019.00745"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Shi, Z., et al.: Crowd counting with deep negative correlation learning. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5382\u20135390 (2018)","DOI":"10.1109\/CVPR.2018.00564"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid CNNs. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 1879\u20131888 (2017)","DOI":"10.1109\/ICCV.2017.206"},{"key":"25_CR15","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1109\/TIP.2019.2928634","volume":"29","author":"VA Sindagi","year":"2020","unstructured":"Sindagi, V.A., Patel, V.M.: HA-CCN: hierarchical attention-based crowd counting network. IEEE Trans. Image Process. 29, 323\u2013335 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR16","unstructured":"Sun, G., Liu, Y., Probst, T., Paudel, D.P., Popovic, N., Gool, L.V.: Boosting crowd counting with transformers (2021). arXiv:2105.10926"},{"key":"25_CR17","unstructured":"Wan, J., Chan, A.: Modeling noisy annotations for crowd counting. In: Advances in Neural Information Processing Systems, vol. 33, pp. 3386\u20133396 (2020)"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 548\u2013558 (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"issue":"3","key":"25_CR19","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","volume":"8","author":"W Wang","year":"2022","unstructured":"Wang, W., et al.: PVT V2: improved baselines with pyramid vision transformer. Comput. Vis. Media 8(3), 415\u2013424 (2022)","journal-title":"Comput. Vis. Media"},{"key":"25_CR20","unstructured":"Xiong, Z., Chai, L., Liu, W., Liu, Y., Ren, S., He, S.: Glance to count: Learning to rank with anchors for weakly-supervised crowd counting (2022). arXiv:2205.14659"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Yang, S., Guo, W., Ren, Y.: Crowdformer: an overlap patching vision transformer for top-down crowd counting. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, pp. 1545\u20131551 (2022)","DOI":"10.24963\/ijcai.2022\/215"},{"key":"25_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-58598-3_1","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Yang","year":"2020","unstructured":"Yang, Y., Li, G., Wu, Z., Su, L., Huang, Q., Sebe, N.: Weakly-supervised crowd counting learns from sorting rather than locations. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 1\u201317. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_1"},{"key":"25_CR23","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: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 589\u2013597 (2016)","DOI":"10.1109\/CVPR.2016.70"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8546-3_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:16:13Z","timestamp":1703531773000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8546-3_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"ISBN":["9789819985456","9789819985463"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8546-3_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"26 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}