{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T19:27:54Z","timestamp":1762543674119,"version":"3.37.3"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government","award":["2014-3-00077-008"],"award-info":[{"award-number":["2014-3-00077-008"]}]},{"name":"Development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3194898","type":"journal-article","created":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T19:33:49Z","timestamp":1659123229000},"page":"79491-79501","source":"Crossref","is-referenced-by-count":8,"title":["Blur-Robust Object Detection Using Feature-Level Deblurring via Self-Guided Knowledge Distillation"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9910-419X","authenticated-orcid":false,"given":"Sung-Jin","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Korea University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6004-4086","authenticated-orcid":false,"given":"Seung-Wook","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Electronic and Communication Engineering, Pukyong National University, Busan, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0319-4467","authenticated-orcid":false,"given":"Seung-Won","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Korea University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-7091","authenticated-orcid":false,"given":"Sung-Jea","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Korea University, Seoul, Republic of Korea"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSP.2018.8524461"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/s20143918"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref6","first-page":"1","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Ren"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.l007\/978-3-319-46448-0_2"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00460"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_15"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref17","first-page":"1","article-title":"Generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref18","article-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv:1804.02767"},{"key":"ref19","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"Hendrycks","year":"2019","journal-title":"arXiv:1903.12261"},{"key":"ref20","article-title":"Benchmarking robustness in object detection: Autonomous driving when winter is coming","author":"Michaelis","year":"2019","journal-title":"arXiv:1907.07484"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.35"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00853"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00397"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref26","article-title":"VRT: A video restoration transformer","author":"Liang","year":"2022","journal-title":"arXiv:2201.12288"},{"key":"ref27","article-title":"Uformer: A general U-shaped transformer for image restoration","author":"Wang","year":"2021","journal-title":"arXiv:2106.03106"},{"key":"ref28","article-title":"Restormer: Efficient transformer for high-resolution image restoration","author":"Zamir","year":"2021","journal-title":"arXiv:2111.09881"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150464"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref31","article-title":"FitNets: Hints for thin deep nets","author":"Romero","year":"2014","journal-title":"arXiv:1412.6550"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00409"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00454"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015565"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58595-2_12"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00567"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2192126"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00175"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref40","first-page":"1","article-title":"Learning efficient object detection models with knowledge distillation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Chen"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00361"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00366"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00059"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6862"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref46","article-title":"MMDetection: Open MMLab detection toolbox and benchmark","author":"Chen","year":"2019","journal-title":"arXiv:1906.07155"},{"key":"ref47","article-title":"Accurate, large minibatch SGD: Training ImageNet in 1 hour","author":"Goyal","year":"2017","journal-title":"arXiv:1706.02677"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09844709.pdf?arnumber=9844709","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T09:19:03Z","timestamp":1706779143000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9844709\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":47,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3194898","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2022]]}}}