{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T22:47:10Z","timestamp":1730328430857,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,17]]},"DOI":"10.1145\/3652628.3652641","type":"proceedings-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T14:36:46Z","timestamp":1716475006000},"page":"78-83","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Few-Shot Object Counting model based on self-support matching and attention mechanism"],"prefix":"10.1145","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-7818-1791","authenticated-orcid":false,"given":"Jian","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Information & Engineering, Jiangxi University of Science & Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0009-7820-375X","authenticated-orcid":false,"given":"Xingling","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Information & Engineering, Jiangxi University of Science & Technology, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6206-450X","authenticated-orcid":false,"given":"Xiangchun","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information & Engineering, Jiangxi University of Science & Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"issue":"4","key":"e_1_3_2_1_1_1","first-page":"1256","article-title":"Public place crowd counting model based on image field division [J]","volume":"38","author":"Yuan Jian","year":"2021","unstructured":"Yuan Jian, Wang Shanshan, Luo Yingwei. Public place crowd counting model based on image field division [J]. Application Research of Computers\/Jisuanji Yingyong Yanjiu, 2021, 38(4):1256-12601280.","journal-title":"Application Research of Computers\/Jisuanji Yingyong Yanjiu"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Liu Y Shi M Zhao Q Point in box out: beyond counting persons in crowds[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2019: 6469-6478.","DOI":"10.1109\/CVPR.2019.00663"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106493"},{"issue":"21","key":"e_1_3_2_1_4_1","first-page":"186","article-title":"Estimation and counting of wheat ears density in field based on deep convolutional neural network [J]","volume":"36","author":"Bao Wenxia","year":"2020","unstructured":"Bao Wenxia, Zhang Xin, Hu Gensheng, Estimation and counting of wheat ears density in field based on deep convolutional neural network [J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(21) :186-193F0003.","journal-title":"Transactions of the Chinese Society of Agricultural Engineering"},{"volume-title":"A large contextual dataset for classification, detection and counting of cars with deep learning[C]\/\/Computer Vision-ECCV 2016: 14th European Conference","year":"2016","author":"Mundhenk T N","key":"e_1_3_2_1_5_1","unstructured":"Mundhenk T N, Konjevod G, Sakla W A, A large contextual dataset for classification, detection and counting of cars with deep learning[C]\/\/Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14. Springer International Publishing, 2016: 785-800."},{"volume-title":"Perth. Australia","year":"2018","author":"Lu E","key":"e_1_3_2_1_6_1","unstructured":"Lu E, Xie W, Zisserman A. Class-agnostic counting[C]\/\/Computer Vision-ACCV 2018: 14th Asian Conference on Computer Vision, Perth. Australia, December 2-6, 2018, Revised Selected Papers, Part III 14. Springer International Publishing, 2019: 669-684."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Yang S D Su H T Hsu W H Class-agnostic few-shot object counting[C]\/\/Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 2021: 870-878.","DOI":"10.1109\/WACV48630.2021.00091"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Ranjan V Sharma U Nguyen T Learning to count everything[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition . 2021: 3394-3403.","DOI":"10.1109\/CVPR46437.2021.00340"},{"volume-title":"Zisserman A","year":"2022","author":"Liu C","key":"e_1_3_2_1_9_1","unstructured":"Liu C, Zhong Y, Zisserman A, Countr: Transformer-based generalized visual counting[J]. arXiv preprint arXiv:2208.13721, 2022."},{"volume-title":"Learning to count anything: Reference-less class-agnostic counting with weak supervision[J]. arXiv preprint arXiv:2205.10203","year":"2022","author":"Hobley M","key":"e_1_3_2_1_10_1","unstructured":"Hobley M, Prisacariu V. Learning to count anything: Reference-less class-agnostic counting with weak supervision[J]. arXiv preprint arXiv:2205.10203, 2022."},{"volume-title":"Few-shot object counting and detection[C]\/\/European Conference on Computer Vision","year":"2022","author":"Nguyen T","key":"e_1_3_2_1_11_1","unstructured":"Nguyen T, Pham C, Nguyen K, Few-shot object counting and detection[C]\/\/European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 348-365."},{"volume-title":"Object counting: you only need to look at one[J]. arXiv preprint arXiv:2112.05993","year":"2021","author":"Lin H","key":"e_1_3_2_1_12_1","unstructured":"Lin H, Hong X, Wang Y. Object counting: you only need to look at one[J]. arXiv preprint arXiv:2112.05993, 2021."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Ranjan V Hoai M. Vicinal counting networks[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022: 4221-4230.","DOI":"10.1109\/CVPRW56347.2022.00467"},{"key":"e_1_3_2_1_14_1","first-page":"701","article-title":"Self-support few-shot semantic segmentation[C]\/\/European Conference on Computer Vision. Cham: Springer Nature Cham: Springer Nature","volume":"2022","author":"Fan Q","unstructured":"Fan Q, Pei W, Tai Y W, Self-support few-shot semantic segmentation[C]\/\/European Conference on Computer Vision. Cham: Springer Nature Cham: Springer Nature, Switzerland, 2022: 701-719.","journal-title":"Switzerland"},{"volume-title":"Attention is all you need[J]. Advances in neural information processing systems","year":"2017","author":"Vaswani A","key":"e_1_3_2_1_15_1","unstructured":"Vaswani A, Shazeer N, Parmar N, Attention is all you need[J]. Advances in neural information processing systems, 2017, 30."},{"issue":"8","key":"e_1_3_2_1_16_1","first-page":"2241","article-title":"A survey of Few-Shot Learning based on deep neural networks [J].","volume":"37","author":"Li Xinye","year":"2020","unstructured":"Li Xinye, Long Shenpeng, Zhu Jing. A survey of Few-Shot Learning based on deep neural networks [J].Application Research of Computers, 2020, 37 (8) :2241- 2247.","journal-title":"Application Research of Computers"},{"volume-title":"Prototypical networks for few-shot learning[J]. Advances in neural information processing systems","year":"2017","author":"Snell J","key":"e_1_3_2_1_17_1","unstructured":"Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning[J]. Advances in neural information processing systems, 2017, 30."},{"volume-title":"On first-order meta-learning algorithms[J]. arXiv preprint arXiv:1803.02999","year":"2018","author":"Nichol A","key":"e_1_3_2_1_18_1","unstructured":"Nichol A, Achiam J, Schulman J. On first-order meta-learning algorithms[J]. arXiv preprint arXiv:1803.02999, 2018."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Fan Q Zhuo W Tang C K Few-shot object detection with attention-RPN and multi-relation detector[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020: 4013-4022.","DOI":"10.1109\/CVPR42600.2020.00407"},{"journal-title":"IEEE Transactions on Circuits and Systems for Video Technology","year":"2023","author":"Jiang S","key":"e_1_3_2_1_20_1","unstructured":". Jiang S, Wang Q, Cheng F, A Unified Object Counting Network with Object Occupation Prior[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023."},{"volume-title":"Zou Y","year":"2019","author":"Wang K","key":"e_1_3_2_1_21_1","unstructured":"Wang K, Liew J H, Zou Y, Panet: Few-shot image semantic segmentation with prototype alignment[C]\/\/proceedings of the IEEE\/CVF international conference on computer vision. 2019: 9197-9206."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Wu Z Shi X Lin G Learning meta-class memory for few-shot semantic segmentation[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2021: 517-526.","DOI":"10.1109\/ICCV48922.2021.00056"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Shi M Lu H Feng C Represent compare and learn: a similarity-aware framework for class-agnostic counting[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022: 9529-9538.","DOI":"10.1109\/CVPR52688.2022.00931"},{"volume-title":"Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting[J]","year":"2022","author":"Lin W","key":"e_1_3_2_1_24_1","unstructured":"Lin W, Yang K, Ma X, Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting[J]. 2022."},{"volume-title":"Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556","year":"2014","author":"Simonyan K","key":"e_1_3_2_1_25_1","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Szegedy C Liu W Jia Y Going deeper with convolutions[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1 -9.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Wan J Liu Z Chan A B. A generalized loss function for crowd counting and localization[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2021: 1974-1983.","DOI":"10.1109\/CVPR46437.2021.00201"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Kang B Liu Z Wang X Few-shot object detection via feature reweighting[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2019: 8420-8429.","DOI":"10.1109\/ICCV.2019.00851"},{"volume-title":"Model-agnostic meta-learning for fast adaptation of deep networks[C]\/\/International conference on machine learning. pmlr","year":"2017","author":"Finn C","key":"e_1_3_2_1_29_1","unstructured":"Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks[C]\/\/International conference on machine learning. pmlr , 2017: 1126-1135."}],"event":{"name":"ICAICE 2023: The 4th International Conference on Artificial Intelligence and Computer Engineering","acronym":"ICAICE 2023","location":"Dalian China"},"container-title":["Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652628.3652641","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T13:30:06Z","timestamp":1716557406000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652628.3652641"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,17]]},"references-count":29,"alternative-id":["10.1145\/3652628.3652641","10.1145\/3652628"],"URL":"https:\/\/doi.org\/10.1145\/3652628.3652641","relation":{},"subject":[],"published":{"date-parts":[[2023,11,17]]},"assertion":[{"value":"2024-05-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}