{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:17:51Z","timestamp":1783570671795,"version":"3.55.0"},"reference-count":34,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876026"],"award-info":[{"award-number":["61876026"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906022"],"award-info":[{"award-number":["61906022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472053"],"award-info":[{"award-number":["61472053"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91420102"],"award-info":[{"award-number":["91420102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61703062"],"award-info":[{"award-number":["61703062"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Foundation for Chongqing Postdoctoral Research","award":["Xm2016060"],"award-info":[{"award-number":["Xm2016060"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2019.2962572","type":"journal-article","created":{"date-parts":[[2019,12,26]],"date-time":"2019-12-26T20:13:53Z","timestamp":1577391233000},"page":"4935-4943","source":"Crossref","is-referenced-by-count":64,"title":["Perspective Transformation Data Augmentation for Object Detection"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7266-6846","authenticated-orcid":false,"given":"Ke","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1955-6626","authenticated-orcid":false,"given":"Bin","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1559-912X","authenticated-orcid":false,"given":"Jiye","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"key":"ref32","article-title":"Recent advances in object detection in the age of deep convolutional neural networks","author":"agarwal","year":"2018","journal-title":"arXiv 1809 03193"},{"key":"ref31","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","article-title":"Slot based image augmentation system for object detection","author":"zhou","year":"2019","journal-title":"arXiv 1907 12900"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref11","first-page":"20","article-title":"Temporal segment networks: Towards good practices for deep action recognition","author":"wang","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.168"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.97"},{"key":"ref14","article-title":"Revisiting batch normalization for practical domain adaptation","author":"li","year":"2016","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref15","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.11.028"},{"key":"ref17","article-title":"Random erasing data augmentation","author":"zhong","year":"2017","journal-title":"arXiv 1708 04896"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2696121"},{"key":"ref19","article-title":"Autoaugment: Learning augmentation policies from data","author":"cubuk","year":"2018","journal-title":"arXiv 1805 09501"},{"key":"ref28","article-title":"Towards principled methods for training generative adversarial networks. arxiv","author":"arjovsky","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref27","article-title":"Progressive growing of Gans for improved quality, stability, and variation","author":"karras","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref3","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref6","first-page":"91","article-title":"Faster R&#x2013;CNN: Towards real&#x2013;time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref7","article-title":"Yolov3: An incremental improvement","author":"redmon","year":"2018","journal-title":"arXiv 1804 02767"},{"key":"ref2","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref9","first-page":"234","article-title":"U&#x2013;Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref20","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref22","article-title":"Deep image: Scaling up image recognition","author":"wu","year":"2015","journal-title":"arXiv 1501 02876 [cs]"},{"key":"ref21","article-title":"Data augmentation generative adversarial networks","author":"antoniou","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref24","first-page":"2731","article-title":"Population based augmentation: Efficient learning of augmentation policy schedules","author":"ho","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref23","first-page":"807","article-title":"Effective training of a neural network character classifier for word recognition","author":"yaeger","year":"1997","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","first-page":"364","article-title":"Modeling visual context is Key to augmenting object detection datasets","author":"dvornik","year":"2018","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref25","article-title":"Learning data augmentation strategies for object detection","author":"zoph","year":"2019","journal-title":"arXiv 1906 11172"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/08943416.pdf?arnumber=8943416","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:50:29Z","timestamp":1639770629000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8943416\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2962572","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}