{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:21:39Z","timestamp":1740100899619,"version":"3.37.3"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672273,61832008"],"award-info":[{"award-number":["61672273,61832008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010880","name":"State Grid Corporation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010880","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"DOI":"10.1109\/icme52920.2022.9859887","type":"proceedings-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T19:45:18Z","timestamp":1661543118000},"page":"1-6","source":"Crossref","is-referenced-by-count":3,"title":["Uncertainty-Based Network for Few-Shot Image Classification"],"prefix":"10.1109","author":[{"given":"Minglei","family":"Yuan","sequence":"first","affiliation":[{"name":"Nanjing University,National Key Laboratory for Novel Software Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanjing University,National Key Laboratory for Novel Software Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhao","family":"Cai","sequence":"additional","affiliation":[{"name":"Nanjing University,National Key Laboratory for Novel Software Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin-Dong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Nanjing University,National Key Laboratory for Novel Software Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University,National Key Laboratory for Novel Software Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Lu","sequence":"additional","affiliation":[{"name":"Nanjing University,National Key Laboratory for Novel Software Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University,National Key Laboratory for Novel Software Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","volume":"1","author":"yarin","year":"2016","journal-title":"Uncertainty in deep learning"},{"key":"ref11","article-title":"In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning","author":"rizve","year":"0","journal-title":"ICLRE"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/471"},{"key":"ref13","article-title":"Information maximization for few-shot learning","author":"boudiaf","year":"0","journal-title":"NeurIPS"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.328"},{"key":"ref15","first-page":"719","article-title":"TADAM: task dependent adaptive metric for improved few-shot learning","author":"oreshkin","year":"0","journal-title":"NeurIPS"},{"key":"ref16","article-title":"Distilling the knowledge in a neural network","volume":"abs 1503 2531","author":"hinton","year":"2015","journal-title":"CoRR"},{"key":"ref17","article-title":"Bayesian active learning for classification and preference learning","volume":"abs 1112 5745","author":"houlsby","year":"2011","journal-title":"CoRR"},{"key":"ref18","article-title":"Meta-learning for semi-supervised few-shot classification","author":"mengye","year":"0","journal-title":"ICLRE"},{"key":"ref19","article-title":"Meta-learning with differentiable closed-form solvers","author":"luca","year":"0","journal-title":"ICLRE"},{"key":"ref28","article-title":"Empirical bayes transductive meta-learning with synthetic gradients","author":"hu","year":"0","journal-title":"ICLRE"},{"key":"ref4","article-title":"Learning to propagate labels: Transductive propagation network for few-shot learning","author":"liu","year":"0","journal-title":"ICLRE"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00815"},{"key":"ref3","first-page":"1199","article-title":"Learning to compare: Relation network for few-shot learning","author":"flood","year":"0","journal-title":"CVPR"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00010"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_24"},{"key":"ref5","article-title":"A baseline for few-shot image classification","author":"dhillon","year":"0","journal-title":"ICLRE"},{"key":"ref8","first-page":"4005","article-title":"Cross attention network for few-shot classification","author":"hou","year":"0","journal-title":"NeurIPS"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01340"},{"key":"ref2","first-page":"3630","article-title":"Matching networks for one shot learning","author":"vinyals","year":"0","journal-title":"NIPS"},{"key":"ref9","article-title":"Transductive few-shot learning with meta-learned confidence","volume":"abs 2002 12017","author":"kye","year":"2020","journal-title":"CoRR"},{"key":"ref1","first-page":"4077","article-title":"Prototypical networks for few-shot learning","author":"snell","year":"0","journal-title":"NIPS"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00948"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"ref21","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume":"70","author":"finn","year":"0","journal-title":"ICML"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_8"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00009"},{"key":"ref25","article-title":"IEPT: instance-level and episode-level pretext tasks for few-shot learning","author":"zhang","year":"0","journal-title":"ICLRE"}],"event":{"name":"2022 IEEE International Conference on Multimedia and Expo (ICME)","start":{"date-parts":[[2022,7,18]]},"location":"Taipei, Taiwan","end":{"date-parts":[[2022,7,22]]}},"container-title":["2022 IEEE International Conference on Multimedia and Expo (ICME)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9859562\/9858923\/09859887.pdf?arnumber=9859887","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T20:24:41Z","timestamp":1663619081000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9859887\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/icme52920.2022.9859887","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}