{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:17:16Z","timestamp":1777501036879,"version":"3.51.4"},"reference-count":99,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["YIP N00014-16-1-3134"],"award-info":[{"award-number":["YIP N00014-16-1-3134"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/tip.2019.2928634","type":"journal-article","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T20:15:17Z","timestamp":1563567317000},"page":"323-335","source":"Crossref","is-referenced-by-count":181,"title":["HA-CCN: Hierarchical Attention-Based Crowd Counting Network"],"prefix":"10.1109","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4192-5547","authenticated-orcid":false,"given":"Vishwanath A.","family":"Sindagi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5239-692X","authenticated-orcid":false,"given":"Vishal M.","family":"Patel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00545"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.07.007"},{"key":"ref33","first-page":"615","article-title":"Towards perspective-free object counting with deep learning","author":"o\u00f1oro-rubio","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_41"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_30"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/2733373.2806337"},{"key":"ref37","first-page":"1","article-title":"In defense of single-column networks for crowd counting","author":"wang","year":"2018","journal-title":"Proc Brit Mach Vis Conf (BMVC)"},{"key":"ref36","first-page":"1","article-title":"Learning short-cut connections for object counting","author":"o\u00f1oro-rubio","year":"2018","journal-title":"Proc Brit Mach Vis Conf (BMVC)"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2967300"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2017.8078491"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-8483-7_14"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2016.7477682"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2014.2358029"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2015.04.006"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2009.22"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2008.4761705"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.329"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.5244\/C.26.21"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.372"},{"key":"ref25","first-page":"1324","article-title":"Learning to count objects in images","author":"lempitsky","year":"2010","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref50","first-page":"5099","article-title":"Context-aware crowd counting","author":"liu","year":"2019","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref51","first-page":"8297","article-title":"Wide-area crowd counting via ground-plane density maps and multi-view fusion CNNs","author":"zhang","year":"2019","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.10"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.667"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00751"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1038\/nrn3916"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1038\/nrn755"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/34.730558"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref52","article-title":"Learning from synthetic data for crowd counting in the wild","author":"wang","year":"2019","journal-title":"arXiv 1903 03303"},{"key":"ref40","article-title":"In defense of single-column networks for crowd counting","author":"wang","year":"2018","journal-title":"arXiv 1808 06133"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.206"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.429"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00564"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00799"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00381"},{"key":"ref49","first-page":"7279","article-title":"Revisiting perspective information for efficient crowd counting","author":"shi","year":"2019","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00550"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_45"},{"key":"ref46","first-page":"12736","article-title":"Leveraging heterogeneous auxiliary tasks to assist crowd counting","author":"zhao","year":"2019","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref45","first-page":"4036","article-title":"Residual regression with semantic prior for crowd counting","author":"wan","year":"2019","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref48","article-title":"Crowd counting and density estimation by trellis encoder&#x2013;decoder network","author":"jiang","year":"2019","journal-title":"arXiv 1903 00853"},{"key":"ref47","article-title":"Inverse attention guided deep crowd counting network","author":"sindagi","year":"2019","journal-title":"arXiv 1907 01193"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_33"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00120"},{"key":"ref44","article-title":"Adcrowdnet: An attention-injective deformable convolutional network for crowd understanding","author":"liu","year":"2018","journal-title":"arXiv 1811 11968"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_25"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.540"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.770"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.239"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.5244\/C.31.20"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298780"},{"key":"ref78","first-page":"1742","article-title":"Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation","author":"papandreou","year":"2015","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2535231"},{"key":"ref60","article-title":"ABC-CNN: An attention based convolutional neural network for visual question answering","author":"chen","year":"2015","journal-title":"arXiv 1511 05960"},{"key":"ref62","first-page":"451","article-title":"Ask, attend and answer: Exploring question-guided spatial attention for visual question answering","author":"xu","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00636"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.601"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref65","first-page":"9401","article-title":"Gather-excite: Exploiting feature context in convolutional neural networks","author":"hu","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref66","article-title":"BAM: Bottleneck attention module","author":"park","year":"2018","journal-title":"arXiv 1807 06514"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00081"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.557"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.70"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.147"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298684"},{"key":"ref95","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"arXiv 1609 02907"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2844853"},{"key":"ref93","first-page":"3546","article-title":"Semi-supervised learning with ladder networks","author":"rasmus","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref92","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","author":"kingma","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2751966"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2891895"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0735-3"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2396051"},{"key":"ref96","first-page":"570","article-title":"A framework for multiple-instance learning","author":"maron","year":"1998","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_17"},{"key":"ref11","first-page":"7323","article-title":"Top-down feedback for crowd counting convolutional neural network","author":"sam","year":"2018","journal-title":"Proc 32nd AAAI Conf Artif Intell"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018868"},{"key":"ref13","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00236"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.366"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8794195"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_22"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2018.00022"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.404"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00135"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.436"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298668"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_22"},{"key":"ref85","first-page":"1","article-title":"Weakly supervised object recognition with convolutional neural networks","author":"oquab","year":"2014","journal-title":"Proc NIPS"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2666739"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.475"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2172800"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/8835130\/08767009.pdf?arnumber=8767009","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T14:38:48Z","timestamp":1651070328000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8767009\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":99,"URL":"https:\/\/doi.org\/10.1109\/tip.2019.2928634","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}