{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T13:29:00Z","timestamp":1778592540840,"version":"3.51.4"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF- 2019R1A2C1006159"],"award-info":[{"award-number":["NRF- 2019R1A2C1006159"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3078295","type":"journal-article","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T22:36:08Z","timestamp":1620426968000},"page":"70797-70805","source":"Crossref","is-referenced-by-count":11,"title":["Optimizing Spatiotemporal Feature Learning in 3D Convolutional Neural Networks With Pooling Blocks"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7777-5996","authenticated-orcid":false,"given":"Rockson","family":"Agyeman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6713-8766","authenticated-orcid":false,"given":"Muhammad","family":"Rafiq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun Kwang","family":"Shin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8793-3828","authenticated-orcid":false,"given":"Bernhard","family":"Rinner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0854-768X","authenticated-orcid":false,"given":"Gyu Sang","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.07.028"},{"key":"ref33","article-title":"ConvNet architecture search for spatiotemporal feature learning","author":"tran","year":"2017","journal-title":"arXiv 1708 05038"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.223"},{"key":"ref31","article-title":"UCF101: A dataset of 101 human actions classes from videos in the wild","author":"soomro","year":"2012","journal-title":"arXiv 1212 0402"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"ref37","first-page":"7138","article-title":"T-C3D: Temporal convolutional 3D network for real-time action recognition","volume":"32","author":"liu","year":"2018","journal-title":"Proc Conf Artif Intell"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/TPAMI.2011.236","article-title":"Motion detail preserving optical flow estimation","volume":"34","author":"xu","year":"2012","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8296599"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref40","first-page":"1","article-title":"Inception-v4, Inception-ResNet and the impact of residual connections on learning","volume":"31","author":"szegedy","year":"2017","journal-title":"Proc Conf Artif Intell"},{"key":"ref11","first-page":"568","article-title":"Two-stream convolutional networks for action recognition in videos","author":"simonyan","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299101"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00155"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.272"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15822-3_20"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.59"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2699229"},{"key":"ref19","article-title":"Wide residual networks","author":"zagoruyko","year":"2016","journal-title":"arXiv 1605 07146"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2017.369"},{"key":"ref4","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00675"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2009.5403089"},{"key":"ref5","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":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref2","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref1","first-page":"255","article-title":"Convolutional networks for images, speech, and time series","volume":"3361","author":"lecun","year":"1995","journal-title":"The Handbook of Brain Theory and Neural Networks"},{"key":"ref46","article-title":"L2 regularization for learning kernels","author":"cortes","year":"2012","journal-title":"arXiv 1205 2653"},{"key":"ref20","article-title":"Efficient training of giant neural networks using pipeline parallelism","author":"huang","year":"2018","journal-title":"Arxiv 1808 07233"},{"key":"ref45","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Proc 12th Symp Operating Syst Design Implement"},{"key":"ref22","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size","author":"iandola","year":"2016","journal-title":"arXiv 1602 07360"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/QoMEX.2016.7498955"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.3390\/s20061702"},{"key":"ref24","first-page":"6848","article-title":"ShuffleNet: An extremely efficient convolutional neural network for mobile devices","author":"zhang","year":"2017","journal-title":"Proc Conf Comput Vis Pattern Recognit"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/MIPR.2019.00055"},{"key":"ref23","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2020.3043710"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00054"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"5097","DOI":"10.3390\/s20185097","article-title":"3D deep learning on medical images: A review","volume":"20","author":"singh","year":"2020","journal-title":"SENSORS"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8621865"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09425533.pdf?arnumber=9425533","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:00Z","timestamp":1639770960000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9425533\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3078295","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}