{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:21:50Z","timestamp":1764588110265,"version":"3.37.3"},"reference-count":70,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) Program with the Technical University of Munich at TUMCREATE","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1109\/tits.2020.3019390","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T21:02:45Z","timestamp":1599253365000},"page":"982-996","source":"Crossref","is-referenced-by-count":13,"title":["A Unified Multi-Task Learning Architecture for Fast and Accurate Pedestrian Detection"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0795-4977","authenticated-orcid":false,"given":"Chengju","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Meiqing","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8346-2635","authenticated-orcid":false,"given":"Siew-Kei","family":"Lam","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_9"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_39"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00811"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00740"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00533"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_38"},{"key":"ref8","article-title":"Small-scale pedestrian detection based on somatic topology localization and temporal feature aggregation","author":"Song","year":"2018","journal-title":"arXiv:1807.01438"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_45"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.5244\/C.31.34"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.474"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.141"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2300479"},{"key":"ref14","first-page":"1","article-title":"CSID: Center, scale, identity and density-aware pedestrian detection in a crowd","volume-title":"Proc. ICCV","author":"Zhang"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00215"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00656"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10584-0_20"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.111"},{"key":"ref23","article-title":"Fused deep neural networks for efficient pedestrian detection","author":"Du","year":"2018","journal-title":"arXiv:1805.08688"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.530"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.155"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00255"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2462456.2464448"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3070203"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.226"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014067"},{"key":"ref34","first-page":"1","article-title":"Learning task grouping and overlap in multi-task learning","volume-title":"Proc. ICML","author":"Kumar"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007327622663"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-2139"},{"key":"ref37","first-page":"1","article-title":"Trace norm regularised deep multi-task learning","volume-title":"Proc. ICLR","author":"Yang"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.433"},{"key":"ref39","article-title":"Multi-task learning by deep collaboration and application in facial landmark detection","author":"Trottier","year":"2017","journal-title":"arXiv:1711.00111"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803687"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00451"},{"key":"ref42","article-title":"An overview of multi-task learning in deep neural networks","author":"Ruder","year":"2017","journal-title":"arXiv:1706.05098"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_7"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2781233"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2017.8078482"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_28"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"ref48","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2017","journal-title":"arXiv:1709.01507"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00731"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2966371"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.343"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.423"},{"key":"ref53","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2750080"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00975"},{"key":"ref58","article-title":"ENet: A deep neural network architecture for real-time semantic segmentation","author":"Paszke","year":"2016","journal-title":"arXiv:1606.02147"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_34"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00941"},{"key":"ref61","article-title":"CrowdHuman: A benchmark for detecting human in a crowd","author":"Shao","year":"2018","journal-title":"arXiv:1805.00123"},{"key":"ref62","article-title":"WIDER face and pedestrian challenge 2018: Methods and results","author":"Change Loy","year":"2019","journal-title":"arXiv:1902.06854"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298784"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref68","article-title":"Memory-efficient implementation of DenseNets","author":"Pleiss","year":"2017","journal-title":"arXiv:1707.06990"},{"volume-title":"A Memory-Efficient Implementation of Densenets","year":"2017","author":"Pleiss","key":"ref69"},{"volume-title":"Optimize Layers Structure of Keras Model to Reduce Computation Time","year":"2018","key":"ref70"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/9701814\/09186837.pdf?arnumber=9186837","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T22:45:38Z","timestamp":1704840338000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9186837\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2]]},"references-count":70,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tits.2020.3019390","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"type":"print","value":"1524-9050"},{"type":"electronic","value":"1558-0016"}],"subject":[],"published":{"date-parts":[[2022,2]]}}}