{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:55:19Z","timestamp":1742957719720,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031189067"},{"type":"electronic","value":"9783031189074"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18907-4_56","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:03:53Z","timestamp":1666825433000},"page":"722-734","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Double Recursive Sparse Self-attention Based Crowd Counting in\u00a0the\u00a0Cluttered Background"],"prefix":"10.1007","author":[{"given":"Boxiang","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Suyu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Sai","family":"Xiao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"56_CR1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, D., Chen, S. et al.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589\u2013597 (2016)","DOI":"10.1109\/CVPR.2016.70"},{"key":"56_CR2","doi-asserted-by":"crossref","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)","DOI":"10.1109\/CVPR.2017.429"},{"key":"56_CR3","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 the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091\u20131100 (2018)","DOI":"10.1109\/CVPR.2018.00120"},{"key":"56_CR4","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"56_CR5","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., et al.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3146\u20133154 (2019)","DOI":"10.1109\/CVPR.2019.00326"},{"key":"56_CR6","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., et al.: CCNet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, vol. 603\u2013612 (2019)","DOI":"10.1109\/ICCV.2019.00069"},{"key":"56_CR7","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., et al.: Self-attention generative adversarial networks. In: International Conference on Machine Learning, pp. 7354\u20137363. PMLR (2019)"},{"key":"56_CR8","doi-asserted-by":"crossref","unstructured":"Rong, L., Li, C.:Coarse-and fine-grained attention network with background-aware loss for crowd density map estimation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3675\u20133684 (2021)","DOI":"10.1109\/WACV48630.2021.00372"},{"key":"56_CR9","unstructured":"Yi, Q., Liu, Y., Jiang, A., et al.: Scale-aware network with regional and semantic attentions for crowd counting under cluttered background. arXiv preprint arXiv:2101.01479 (2021)"},{"key":"56_CR10","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.1109\/LSP.2021.3096119","volume":"28","author":"W Xu","year":"2021","unstructured":"Xu, W., Liang, D., Zheng, Y., et al.: Dilated-scale-aware category-attention convnet for multi-class object counting. IEEE Signal Process. Lett. 28, 1570\u20131574 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"56_CR11","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"56_CR12","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"56_CR13","unstructured":"Chu, X., Tian, Z., Wang, Y., et al.: Twins: Revisiting the design of spatial attention in vision transformers. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"key":"56_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"56_CR15","doi-asserted-by":"crossref","unstructured":"Liu, W., Salzmann, M., Fua, P.: Context-aware crowd counting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5099\u20135108 (2019)","DOI":"10.1109\/CVPR.2019.00524"},{"key":"56_CR16","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"56_CR17","doi-asserted-by":"crossref","unstructured":"Idrees, H., Tayyab, M., Athrey, K., et al.: Composition loss for counting, density map estimation and localization in dense crowds. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 532\u2013546 (2018)","DOI":"10.1007\/978-3-030-01216-8_33"},{"key":"56_CR18","doi-asserted-by":"crossref","unstructured":"Idrees, H., Saleemi, I., Seibert, C., et al.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547\u20132554 (2013)","DOI":"10.1109\/CVPR.2013.329"},{"key":"56_CR19","doi-asserted-by":"crossref","unstructured":"Wang, Q., Gao, J., Lin, W., et al.: Learning from synthetic data for crowd counting in the wild. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8198\u20138207 (2019)","DOI":"10.1109\/CVPR.2019.00839"},{"key":"56_CR20","doi-asserted-by":"crossref","unstructured":"Yan, Z., Yuan, Y., Zuo, W., et al.: Perspective-guided convolution networks for crowd counting. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 952\u2013961 (2019)","DOI":"10.1109\/ICCV.2019.00104"},{"issue":"07","key":"56_CR21","first-page":"11765","volume":"34","author":"Y Miao","year":"2020","unstructured":"Miao, Y., Lin, Z., Ding, G., et al.: Shallow feature based dense attention network for crowd counting. Proc. AAAI Conf. Artif. Intell. 34(07), 11765\u201311772 (2020)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"56_CR22","doi-asserted-by":"crossref","unstructured":"Yang, Y., Li, G., Wu, Z., et al.: Reverse perspective network for perspective-aware object counting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4374\u20134383 (2020)","DOI":"10.1109\/CVPR42600.2020.00443"},{"key":"56_CR23","unstructured":"Chen, G., Guo, P.: Enhanced information fusion network for crowd counting. arXiv preprint arXiv:2101.04279 (2021)"},{"issue":"6","key":"56_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-021-3445-y","volume":"65","author":"D Liang","year":"2022","unstructured":"Liang, D., Chen, X., Xu, W., et al.: Transcrowd: weakly-supervised crowd counting with transformers. Sci. China Inf. Sci. 65(6), 1\u201314 (2022)","journal-title":"Sci. China Inf. Sci."},{"key":"56_CR25","doi-asserted-by":"crossref","unstructured":"Wan, J., Liu, Z., Chan, A.B.: A generalized loss function for crowd counting and localization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1974\u20131983 (2021)","DOI":"10.1109\/CVPR46437.2021.00201"}],"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\/978-3-031-18907-4_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:13:51Z","timestamp":1666826031000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18907-4_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189067","9783031189074"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18907-4_56","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 October 2022","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":"Shenzhen","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.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","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","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":"233","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":"41% - 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":"3.03","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":"3.35","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)"}}]}}