{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:46:06Z","timestamp":1743554766962,"version":"3.37.3"},"reference-count":33,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100010418","name":"Institute for Information Communications Technology Promotion (IITP)","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,19]]},"DOI":"10.1109\/ictc55196.2022.9953011","type":"proceedings-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T22:41:09Z","timestamp":1669416069000},"page":"467-470","source":"Crossref","is-referenced-by-count":2,"title":["Supervised vs. Self-supervised Pre-trained models for Hand Pose Estimation"],"prefix":"10.1109","author":[{"given":"Gyusang","family":"Cho","sequence":"first","affiliation":[{"name":"Electrical Engineering KAIST,Daejeon,Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chan-Hyun","family":"Youn","sequence":"additional","affiliation":[{"name":"Electrical Engineering KAIST,Daejeon,Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.525"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00539"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref30","article-title":"Barlow twins: Self-supervised learning via redundancy reduction","author":"zbontar","year":"2021","journal-title":"ICML"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00537"},{"key":"ref11","article-title":"Real-time hand tracking under occlusion from an egocentric rgb-d sensor","author":"sotnychenko","year":"2017","journal-title":"IEEE International Conference on Computer Vision (ICCV)"},{"key":"ref12","article-title":"Bootstrap your own latent-a new approach to self-supervised learning","author":"grill","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref13","article-title":"Momentum contrast for unsupervised visual representation learning","author":"he","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00502"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref16","article-title":"Pay attention to features, transfer learn faster cnns","author":"zhao","year":"2020","journal-title":"ICLRE"},{"journal-title":"Do Better ImageNet Models Transfer Better?","year":"2018","author":"kornblith","key":"ref17"},{"journal-title":"Cifar-10 (cana-dian institute for advanced research)","year":"2010","author":"krizhevsky","key":"ref18"},{"key":"ref19","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"NeurIPS"},{"key":"ref28","article-title":"Unsupervised object-level representation learning from scene images","author":"xie","year":"2021","journal-title":"NeurIPS"},{"journal-title":"Deeplab Semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs","year":"2016","author":"chen","key":"ref4"},{"key":"ref27","article-title":"Hand keypoint detection in single images using multiview bootstrapping","author":"matthews","year":"2017","journal-title":"IEEE Conf Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref3","article-title":"A broad study on the transferability of visual representations with contrastive learning","author":"panda","year":"2021","journal-title":"[CCV"},{"key":"ref6","article-title":"Big self-supervised models are strong semi-supervised learners","author":"chen","year":"2020","journal-title":"NeurIPS"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00577"},{"key":"ref5","article-title":"A simple framework for contrastive learning of visual representations","author":"chen","year":"2020","journal-title":"ICML"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"ref7","article-title":"Im-proved baselines with momentum contrastive learning","author":"chen","year":"2020","journal-title":"ArXiv Preprint"},{"journal-title":"Vissl a library for state-of-the-art self-supervised learning from images","year":"0","key":"ref2"},{"key":"ref9","article-title":"An analysis of single-layer networks in unsupervised feature learning","author":"coates","year":"2011","journal-title":"AISTATS JMLR Workshop and Conference Proceedings"},{"journal-title":"A Survey on Deep Transfer Learning","year":"0","key":"ref1"},{"key":"ref20","first-page":"1485","author":"marcel","year":"2010","journal-title":"Torchvision the Machine-Vision Package of Torch"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00013"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00674"},{"key":"ref24","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"NeurIPS"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.07.025"},{"key":"ref25","article-title":"Real-time joint tracking of a hand manipulating an object from rgb-d input","author":"sridhar","year":"2016","journal-title":"Proceedings of European Conference on Computer Vision (ECCV)"}],"event":{"name":"2022 13th International Conference on Information and Communication Technology Convergence (ICTC)","start":{"date-parts":[[2022,10,19]]},"location":"Jeju Island, Korea, Republic of","end":{"date-parts":[[2022,10,21]]}},"container-title":["2022 13th International Conference on Information and Communication Technology Convergence (ICTC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9952188\/9952355\/09953011.pdf?arnumber=9953011","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T20:00:57Z","timestamp":1671480057000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9953011\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,19]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/ictc55196.2022.9953011","relation":{},"subject":[],"published":{"date-parts":[[2022,10,19]]}}}